1. Field of the Invention.
This invention relates in general to a data channel, and more particularly to a method for automating the convergence of tap weights in an equalizer for a data channel.
2. Description of Related Art.
Computers often include auxiliary memory storage having media on which data can be written and from which data can be read. Disk drive units incorporating stacked, commonly rotated rigid magnetic disks are one example of storage media. Tape drives and optical storage units are other examples of storage media.
In a disk drive, the data are stored in magnetic form on the disk surfaces. Data are recorded in concentric, radially spaced data information tracks arrayed on the surfaces of the disks. Transducer heads driven in a path toward and away from the drive axis write data to the disks and read data from the disks.
To achieve high data density in writing and reading data on storage media a partial response maximum likelihood (PRML) channel is often used. To obtain full advantage of the PRML channel, the received signal or the read signal must be filtered through a specially designed equalizing filter. For example, a common problem encountered when electronically reading or transmitting data is that it becomes corrupted by such things as background noise, impulse noise, fades, etc. Usually this data corruption is statistical phenomenon which causes additive and/or multiplicative transformations to the originally transmitted data. Thus, the original data undergoes changes such as frequency translation, non-linear or harmonic distortion, and time dispersion. In addition, high speed data transmission over channels of limited bandwidth results in a type of distortion commonly referred to as intersymbol interference.
One technique for reducing intersymbol interference includes equalizing the data using an equalizer that compensates for the average of the range of expected channel amplitude and delay characteristics. However, adaptive equalizers suffer from a relatively long convergence time for a least means square (LMS) algorithm. Another limitation of equalizers is that since they are implemented in digital circuitry, the data must be quantized prior to being processed by, for example, a finite impulse response (FIR) filter.
Current method of equalization for PRML channels involve tester software that executes a sequence of steps for optimizing the taps of the filter. For example, a set of tap weights are loaded into a finite impulse response filter (FIR) of the channel. A measurement window is set with a start and stop byte count which defines the data zone that will be used to make a measurement. A read command is then issued to the file. During the read command, the channel measures the mean squared error of the data that is inside the measurement window.
After the read completes, the value of the error is read into the tester software. The tester software then modifies the tap weights using one of several possible convergence methods and re-loads new tap weights into the channel. A new measurement is then made. If the error is smaller, then the new taps are stored and a new trial is attempted. Eventually, the optimum taps are obtained and the algorithm stops.
Nevertheless, this process is complicated and requires a tester to operate. Further the process is time consuming and the tester software must be rewritten for each new product.
It can be seen that there is a need for a simple, automated equalization method.
It can also be seen that there is a need for an automated equalization method that is contained within the channel itself.